PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping

Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly complex, difficult to reimplement and maintain, and pose high barriers for users without substantial computational expertise. To address these challenges, we introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction. PhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction, data visualisation and automated model training. We validate PhenoAssistant through several representative case studies and a set of evaluation tasks. By significantly lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology.

@article{chen2025_2504.19818,
  title={ PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping },
  author={ Feng Chen and Ilias Stogiannidis and Andrew Wood and Danilo Bueno and Dominic Williams and Fraser Macfarlane and Bruce Grieve and Darren Wells and Jonathan A. Atkinson and Malcolm J. Hawkesford and Stephen A. Rolfe and Tracy Lawson and Tony Pridmore and Mario Valerio Giuffrida and Sotirios A. Tsaftaris },
  journal={arXiv preprint arXiv:2504.19818},
  year={ 2025 }
}